基于nmle包的R中多层次模型

我正在使用

nlme

软件包学习多层次模型，并遵循教科书“使用R发现统计数据”中的示例

该数据集为蜜月

Period.dat

，也可在其同伴网站下载

require（nlme）
要求（2）
satisfactionData=read.delim（“蜜月期.dat”，标题=TRUE）
我已经看过这本书了。看来编码是错误的。您得到的错误是因为您的时间变量是一个因子，您需要它是数字。在书中作者的第一个数字中，他将时间表示为数字（0-3），但他的模型代码不正确。我已为您重新编码：

## First, Time needs to be recoded as a numeric

restructuredData$Time.Numeric <- with(restructuredData, ifelse(Time == "Satisfaction_Base", 0, 
        ifelse(Time == "Satisfaction_6_Months", 1,
        ifelse(Time == "Satisfaction_12_Months", 2,
        ifelse(Time == "Satisfaction_18_Months", 3, NA)))))

## Baseline Model

intercept <-gls(Life_Satisfaction~1, data = restructuredData, method = "ML", na.action = na.exclude)

summary(intercept)

## Model where intercept can vary for Individuals

randomIntercept <- lme(Life_Satisfaction ~ 1, data = restructuredData, random = ~1|Person, method = "ML", na.action = na.exclude, control = list(opt = "optim"))

summary(randomIntercept)

## Add time as a fixed effect

timeRI <- lme(Life_Satisfaction ~ Time.Numeric, data = restructuredData, random = ~1|Person, method = "ML", na.action = na.exclude, control = list(opt = "optim"))

summary(timeRI)

## Add a random slope to the model by nesting the Individual within the test time

timeRS <- lme(Life_Satisfaction ~ Time.Numeric, data = restructuredData, random = ~Time.Numeric|Person, method = "ML", na.action = na.exclude, control = list(opt = "optim"))

summary(timeRS)


## Modeling the covariance structure structure of the errors with a first-order autoregressive covariance structure

ARModel <- update(timeRS, correlation = corAR1(0, form = ~Time.Numeric|Person))

summary(ARModel)

anova(intercept, randomIntercept, timeRI, timeRS, ARModel)

##首先，时间需要重新编码为数字
我已经看过这本书了。看来编码是错误的。您得到的错误是因为您的时间变量是一个因子，您需要它是数字。在书中作者的第一个数字中，他将时间表示为数字（0-3），但他的模型代码不正确。我已为您重新编码：

## First, Time needs to be recoded as a numeric

restructuredData$Time.Numeric <- with(restructuredData, ifelse(Time == "Satisfaction_Base", 0, 
        ifelse(Time == "Satisfaction_6_Months", 1,
        ifelse(Time == "Satisfaction_12_Months", 2,
        ifelse(Time == "Satisfaction_18_Months", 3, NA)))))

## Baseline Model

intercept <-gls(Life_Satisfaction~1, data = restructuredData, method = "ML", na.action = na.exclude)

summary(intercept)

## Model where intercept can vary for Individuals

randomIntercept <- lme(Life_Satisfaction ~ 1, data = restructuredData, random = ~1|Person, method = "ML", na.action = na.exclude, control = list(opt = "optim"))

summary(randomIntercept)

## Add time as a fixed effect

timeRI <- lme(Life_Satisfaction ~ Time.Numeric, data = restructuredData, random = ~1|Person, method = "ML", na.action = na.exclude, control = list(opt = "optim"))

summary(timeRI)

## Add a random slope to the model by nesting the Individual within the test time

timeRS <- lme(Life_Satisfaction ~ Time.Numeric, data = restructuredData, random = ~Time.Numeric|Person, method = "ML", na.action = na.exclude, control = list(opt = "optim"))

summary(timeRS)


## Modeling the covariance structure structure of the errors with a first-order autoregressive covariance structure

ARModel <- update(timeRS, correlation = corAR1(0, form = ~Time.Numeric|Person))

summary(ARModel)

anova(intercept, randomIntercept, timeRI, timeRS, ARModel)

##首先，时间需要重新编码为数字
重组数据$Time.Numeric好建议！我简化了一点：
restructuredData[，“Time”]好建议！我简化了一点：
restructuredData[，“Time”]